2026-10-09T11:17:36Z https://www.scidb.cn/oai

10.57760/sciencedb.0143o 2026-09-29T15:55:08Z

2026-09-29 Dataset on the Impact of Speech Machine Emotion Learning on Caregivers' Work Behavior in Elderly Care doi:10.57760/sciencedb.0143o en This dataset accompanies a three-wave empirical study on how speech machine emotion learning (SMEL) influences the proactive behavior of elderly-care caregivers, grounded in cognitive-affective systems theory. Data were collected via the SoJump platform from caregivers in elderly-care institutions across Shandong and Liaoning provinces through a three-time-point survey (T1: March 2026; T2 and T3: April 2026, two weeks apart), yielding 306 valid matched samples (91.3% matching rate) after distributing 450 questionnaires. The sample is predominantly female (92.2%), with a mean age of 40.87 years (SD = 10.47), and includes caregivers with varying work experience (26.8% under 3 years, 36.6% 3–6 years, 36.6% over 6 years), education levels (24.2% junior high or below, 46.1% high school/vocational, 29.7% associate degree or above), and institution types (38.6% public, 61.4% private). The dataset contains item-level responses on 5-point Likert scales for seven core constructs—speech machine emotion learning (13 items, α = 0.972), perceived controllability (4 items, α = 0.916), self-efficacy (5 items, α = 0.925), attitude toward using AI (4 items, α = 0.912), work passion (7 items, α = 0.951), employee proactive behavior (6 items, α = 0.938), and perceived organizational support (8 items, α = 0.951)—along with demographic control variables (gender, age, years of work experience, education level, and enterprise category). The data support a moderated dual-path mediation model showing that SMEL promotes proactive behavior through a cognitive path (perceived controllability → attitude toward using AI) and a motivational path (self-efficacy → work passion), with perceived organizational support positively moderating both pathways; the dataset is suitable for research on AI in organizational behavior, human-computer interaction, elderly-care service management, and empirical tests of cognitive-affective systems theory. speech machine emotion learning; perceived controllability; self-efficacy; attitude toward using AI; work passion; employee proactive behavior; perceived organizational support; elderly-care caregivers; cognitive-affective systems theory; human-computer interaction Yaqi Cui RESTRICTED dataset Science Data Bank